import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.exp(lowercase				)
				__lowercase					     =				torch.sum(lowercase				,     dim=1				)  # sum of exp(x_i)
				__lowercase					     =				torch.sum(x * exp_x				,     dim=1				)  # sum of x_i * exp(x_i)
				return torch.log(lowercase				) - B / A




class   _UpperCamelCase (		nn.Module					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							__lowercase					     =				config.output_attentions
							__lowercase					     =				config.output_hidden_states
							__lowercase					     =				nn.ModuleList([BertLayer(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)
							__lowercase					     =				nn.ModuleList([BertHighway(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)

							__lowercase					     =				[-1 for _ in range(config.num_hidden_layers		)]





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if (type(lowerCAmelCase__		) is float) or (type(lowerCAmelCase__		) is int):
											for i in range(len(self.early_exit_entropy		)		):
															__lowercase					     =				x
							else:
											__lowercase					     =				x





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							__lowercase					     =				pooler.state_dict()
							for highway in self.highway:
											for name, param in highway.pooler.state_dict().items():
															param.copy_(loaded_model[name]		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  )	->   int:

							'''simple docstring'''


							__lowercase					     =				()
							__lowercase					     =				()
							__lowercase					     =				()
							for i, layer_module in enumerate(self.layer		):
											if self.output_hidden_states:
															__lowercase					     =				all_hidden_states + (hidden_states,)

											__lowercase					     =				layer_module(
											    lowerCAmelCase__ ,  lowerCAmelCase__ ,  head_mask[i] ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				layer_outputs[0]

											if self.output_attentions:
															__lowercase					     =				all_attentions + (layer_outputs[1],)

											__lowercase					     =				(hidden_states,)
											if self.output_hidden_states:
															__lowercase					     =				current_outputs + (all_hidden_states,)
											if self.output_attentions:
															__lowercase					     =				current_outputs + (all_attentions,)

											__lowercase					     =				self.highway[i](lowerCAmelCase__		)
											# logits, pooled_output

											if not self.training:
															__lowercase					     =				highway_exit[0]
															__lowercase					     =				entropy(lowerCAmelCase__		)
															__lowercase					     =				highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
															__lowercase					     =				all_highway_exits + (highway_exit,)

															if highway_entropy < self.early_exit_entropy[i]:
																			__lowercase					     =				(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																			raise HighwayException(lowerCAmelCase__ ,  i + 1		)
											else:
															__lowercase					     =				all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
											__lowercase					     =				all_hidden_states + (hidden_states,)

							__lowercase					     =				(hidden_states,)
							if self.output_hidden_states:
											__lowercase					     =				outputs + (all_hidden_states,)
							if self.output_attentions:
											__lowercase					     =				outputs + (all_attentions,)

							__lowercase					     =				outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''		,_UpperCAmelCase		,)
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__		)
							__lowercase					     =				config

							__lowercase					     =				BertEmbeddings(lowerCAmelCase__		)
							__lowercase					     =				DeeBertEncoder(lowerCAmelCase__		)
							__lowercase					     =				BertPooler(lowerCAmelCase__		)

							self.init_weights()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							self.encoder.init_highway_pooler(self.pooler		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							return self.embeddings.word_embeddings





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							__lowercase					     =				value





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							for layer, heads in heads_to_prune.items():
											self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__		)





			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  )	->   str:

							'''simple docstring'''


							if input_ids is not None and inputs_embeds is not None:
											raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
							elif input_ids is not None:
											__lowercase					     =				input_ids.size()
							elif inputs_embeds is not None:
											__lowercase					     =				inputs_embeds.size()[:-1]
							else:
											raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

							__lowercase					     =				input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
											__lowercase					     =				torch.ones(lowerCAmelCase__ ,  device=lowerCAmelCase__		)
							if encoder_attention_mask is None:
											__lowercase					     =				torch.ones(lowerCAmelCase__ ,  device=lowerCAmelCase__		)
							if token_type_ids is None:
											__lowercase					     =				torch.zeros(lowerCAmelCase__ ,  dtype=torch.long ,  device=lowerCAmelCase__		)

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							__lowercase					     =				self.get_extended_attention_mask(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
											__lowercase					     =				encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
											__lowercase					     =				encoder_attention_mask[:, None, None, :]

							__lowercase					     =				encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
							__lowercase					     =				(1.0 - encoder_extended_attention_mask) * -1_0000.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							__lowercase					     =				self.get_head_mask(lowerCAmelCase__ ,  self.config.num_hidden_layers		)

							__lowercase					     =				self.embeddings(
							    input_ids=lowerCAmelCase__ ,  position_ids=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  inputs_embeds=lowerCAmelCase__		)
							__lowercase					     =				self.encoder(
							    lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  head_mask=lowerCAmelCase__ ,  encoder_hidden_states=lowerCAmelCase__ ,  encoder_attention_mask=lowerCAmelCase__ ,  )
							__lowercase					     =				encoder_outputs[0]
							__lowercase					     =				self.pooler(lowerCAmelCase__		)

							__lowercase					     =				(
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				message
							__lowercase					     =				exit_layer  # start from 1!




class   _UpperCamelCase (		nn.Module					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							super().__init__()
							__lowercase					     =				BertPooler(lowerCAmelCase__		)
							__lowercase					     =				nn.Dropout(config.hidden_dropout_prob		)
							__lowercase					     =				nn.Linear(config.hidden_size ,  config.num_labels		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				encoder_outputs[0]
							__lowercase					     =				self.pooler(lowerCAmelCase__		)
							# "return" pooler_output

							# BertModel
							__lowercase					     =				(pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							__lowercase					     =				bmodel_output[1]

							__lowercase					     =				self.dropout(lowerCAmelCase__		)
							__lowercase					     =				self.classifier(lowerCAmelCase__		)

							return logits, pooled_output




@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''		,_UpperCAmelCase		,)
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__		)
							__lowercase					     =				config.num_labels
							__lowercase					     =				config.num_hidden_layers

							__lowercase					     =				DeeBertModel(lowerCAmelCase__		)
							__lowercase					     =				nn.Dropout(config.hidden_dropout_prob		)
							__lowercase					     =				nn.Linear(config.hidden_size ,  self.config.num_labels		)

							self.init_weights()





			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__=-1 ,  lowerCAmelCase__=False ,  )	->   int:

							'''simple docstring'''


							__lowercase					     =				self.num_layers
							try:
											__lowercase					     =				self.bert(
											    lowerCAmelCase__ ,  attention_mask=lowerCAmelCase__ ,  token_type_ids=lowerCAmelCase__ ,  position_ids=lowerCAmelCase__ ,  head_mask=lowerCAmelCase__ ,  inputs_embeds=lowerCAmelCase__ ,  )
											# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

											__lowercase					     =				outputs[1]

											__lowercase					     =				self.dropout(lowerCAmelCase__		)
											__lowercase					     =				self.classifier(lowerCAmelCase__		)
											__lowercase					     =				(logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
											__lowercase					     =				e.message
											__lowercase					     =				e.exit_layer
											__lowercase					     =				outputs[0]

							if not self.training:
											__lowercase					     =				entropy(lowerCAmelCase__		)
											__lowercase					     =				[]
											__lowercase					     =				[]
							if labels is not None:
											if self.num_labels == 1:
															#  We are doing regression
															__lowercase					     =				MSELoss()
															__lowercase					     =				loss_fct(logits.view(-1		) ,  labels.view(-1		)		)
											else:
															__lowercase					     =				CrossEntropyLoss()
															__lowercase					     =				loss_fct(logits.view(-1 ,  self.num_labels		) ,  labels.view(-1		)		)

											# work with highway exits
											__lowercase					     =				[]
											for highway_exit in outputs[-1]:
															__lowercase					     =				highway_exit[0]
															if not self.training:
																			highway_logits_all.append(lowerCAmelCase__		)
																			highway_entropy.append(highway_exit[2]		)
															if self.num_labels == 1:
																			#  We are doing regression
																			__lowercase					     =				MSELoss()
																			__lowercase					     =				loss_fct(highway_logits.view(-1		) ,  labels.view(-1		)		)
															else:
																			__lowercase					     =				CrossEntropyLoss()
																			__lowercase					     =				loss_fct(highway_logits.view(-1 ,  self.num_labels		) ,  labels.view(-1		)		)
															highway_losses.append(lowerCAmelCase__		)

											if train_highway:
															__lowercase					     =				(sum(highway_losses[:-1]		),) + outputs
															# exclude the final highway, of course
											else:
															__lowercase					     =				(loss,) + outputs
							if not self.training:
											__lowercase					     =				outputs + ((original_entropy, highway_entropy), exit_layer)
											if output_layer >= 0:
															__lowercase					     =				(
															    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
															)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)